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As Machine Learning technologies become increasingly used in contexts that affect citizens, companies as well as researchers need to be confident that their application of these methods will not have unexpected social implications, such as bias towards gender, ethnicity, and/or people with disabilities.
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Discrimination aware decision tree learning
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Transfer learning
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“But the data is already public”: on the ethics of research in Facebook
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Six provocations for big data
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k-NN as an implementation of situation testing for discrimination discovery and prevention
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Fairness-Aware Classifier with Prejudice Remover Regularizer
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma · 2012
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Ethical decision-making and Internet research: Version 2.0
Annette Markham and Others · 2012
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A study of top-k measures for discrimination discovery
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Assessing the bias in samples of large online networks
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Generative adversarial nets
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Yasser Jafer, Stan Matwin, and Marina Sokolova · 2014
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Eric L. Lee, Jing-Kai Lou, Wei-Ming Chen, Yen-Chi Chen, Shou-De Lin, Yen-Sheng Chiang, and Kuan-Ta Chen · 2014
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Big data: Seizing opportunities, preserving values
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A multidisciplinary survey on discrimination analysis
Andrea Romei and Salvatore Ruggieri · 2014
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Andrew Guthrie Ferguson · 2015
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Efficient and robust automated machine learning
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Exploratory Visualization Design Towards Online Social Network Privacy and Data Literacy
Bo Gao · 2015
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What recommenders recommend: an analysis of recommendation biases and possible countermeasures
Dietmar Jannach, Lukas Lerche, Iman Kamehkhosh, and Michael Jugovac · 2015
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On the relation between accuracy and fairness in binary classification
Indre Zliobaite · 2015
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AzureML: Anatomy of a machine learning service
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Big data’s disparate impact
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Man is to computer programmer as woman is to homemaker? Debiasing word embeddings
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How the machine ‘thinks’: Understanding opacity in machine learning algorithms
Jenna Burrell · 2016
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L Elisa Celis, Amit Deshpande, Tarun Kathuria, and Nisheeth K Vishnoi · 2016
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Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
Anupam Datta, Shayak Sen, and Yair Zick · 2016
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A confidence-based approach for balancing fairness and accuracy
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Bryce W Goodman · 2016
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Moritz Hardt, Eric Price, Nati Srebro, and Others · 2016
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Impartial predictive modeling: Ensuring fairness in arbitrary models
Kory D Johnson, Dean P Foster, and Robert A Stine · 2016
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Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2016
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Learning to pivot with adversarial networks, 2016
Gilles Louppe, Michael Kagan, and Kyle Cranmer · 2016
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A statistical framework for fair predictive algorithms
Kristian Lum and James Johndrow · 2016
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Where are human subjects in big data research? The emerging ethics divide
Jacob Metcalf and Kate Crawford · 2016
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Big data: a report on algorithmic systems, opportunity, and civil rights
C Munoz, M Smith, and D Patil · 2016
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Weapons of math destruction: How big data increases inequality and threatens democracy
Cathy O’Neil · 2016
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Information-theoretic analysis of stability and bias of learning algorithms
Maxim Raginsky, Alexander Rakhlin, Matthew Tsao, Yihong Wu, and Aolin Xu · 2016
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Learning Adversarially Fair and Transferable Representations
David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel · 2018
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A neural network framework for fair classifier
P Manisha and Sujit Gujar · 2018
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Algorithmic pollution: Understanding and responding to negative consequences of algorithmic decision-making
Olivera Marjanovic, Dubravka Cecez-Kecmanovic, and Richard Vidgen · 2018
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Preventing disparities: Bayesian and frequentist methods for assessing fairness in machinelearning decision-support models
Douglas S McNair · 2018
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Prediction-based decisions and fairness: A catalogue of choices, assumptions, and definitions
Shira Mitchell, Eric Potash, Solon Barocas, Alexander D’Amour, and Kristian Lum · 2018
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Tal Zarsky · 2016
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Identifying significant predictive bias in classifiers
Zhe Zhang and Daniel B Neill · 2016
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TENDER SPECIFICATIONS: Study on Algorithmic Awareness Building SMART 2017/0055, 2017
2017
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Fairsquare: probabilistic verification of program fairness
Aws Albarghouthi, Loris D’Antoni, Samuel Drews, and Aditya V Nori · 2017
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Penalizing unfairness in binary classification
Yahav Bechavod and Katrina Ligett · 2017
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A convex framework for fair regression
Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth · 2017
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Data decisions and theoretical implications when adversarially learning fair representations
Alex Beutel, Jilin Chen, Zhe Zhao, and Ed H Chi · 2017
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Fair inference on outcomes
Razieh Nabi and Ilya Shpitser · 2018
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Learning with complex loss functions and constraints
Harikrishna Narasimhan · 2018
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Reducing Gender Bias in Abusive Language Detection
Ji Ho Park, Jamin Shin, and Pascale Fung · 2018
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On measuring bias in online information
Evaggelia Pitoura, Panayiotis Tsaparas, Giorgos Flouris, Irini Fundulaki, Panagiotis Papadakos, Serge Abiteboul, and Gerhard Weikum · 2018
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Privacy preserving clustering with constraints
Clemens Rösner and Melanie Schmidt · 2018
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Aequitas: A bias and fairness audit toolkit
Pedro Saleiro, Benedict Kuester, Loren Hinkson, Jesse London, Abby Stevens, Ari Anisfeld, Kit T Rodolfa, and Rayid Ghani · 2018
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Fairness of Exposure in Rankings
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Integrating technical and legal concepts of privacy
Ana Sokolovska and Ljupco Kocarev · 2018
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A Unified Approach to Quantifying Algorithmic Unfairness
Till Speicher, Hoda Heidari, Nina Grgic-Hlaca, Krishna P. Gummadi, Adish Singla, Adrian Weller, and Muhammad Bilal Zafar · 2018
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Calibrated Recommendations
Harald Steck · 2018
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Enhancing the accuracy and fairness of human decision making
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Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making
Michael Veale, Max Van Kleek, and Reuben Binns · 2018
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Fairness definitions explained
Sahil Verma and Julia Rubin · 2018
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Achieving fairness through adversarial learning: an application to recidivism prediction
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Avoiding disparate impact with counterfactual distributions
Hao Wang, Berk Ustun, Flavio P Calmon, and SEAS Harvard · 2018
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Fairgan: Fairness-aware generative adversarial networks
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Finding quasi-identifiers for k-anonymity model by the set of cut-vertex
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Algorithmic regulation: A critical interrogation
Karen Yeung · 2018
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Mitigating unwanted biases with adversarial learning
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Fairness in reciprocal recommendations: A speed-dating study
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Fairness-aware tensor-based recommendation
Ziwei Zhu, Xia Hu, and James Caverlee · 2018
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One-network adversarial fairness
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Fair regression: Quantitative definitions and reduction-based algorithms
Alekh Agarwal, Miroslav Dudík, and Zhiwei Steven Wu · 2019
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Learning optimal and fair decision trees for non-discriminative decision-making
Sina Aghaei, Mohammad Javad Azizi, and Phebe Vayanos · 2019
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Scalable fair clustering
Arturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali Vakilian, and Tal Wagner · 2019
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Fairness and Machine Learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2019
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Probabilistic verification of fairness properties via concentration
Osbert Bastani, Xin Zhang, and Armando Solar-Lezama · 2019
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Racial categories in machine learning
Sebastian Benthall and Bruce D Haynes · 2019
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Fair algorithms for clustering
Suman Bera, Deeparnab Chakrabarty, Nicolas Flores, and Maryam Negahbani · 2019
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Accuracy and fairness for juvenile justice risk assessments
Richard Berk · 2019
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Putting fairness principles into practice: Challenges, metrics, and improvements
Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Allison Woodruff, Christine Luu, Pierre Kreitmann, Jonathan Bischof, and Ed H Chi · 2019
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Google automl: Cloud vision
Ekaba Bisong · 2019
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Improved adversarial learning for fair classification
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Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees
L. Elisa Celis, Lingxiao Huang, Vijay Keswani, and Nisheeth K. Vishnoi · 2019
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Equality of Voice: Towards Fair Representation in Crowdsourced Top-K Recommendations
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Proportionally fair clustering
Xingyu Chen, Brandon Fain, Liang Lyu, and Kamesh Munagala · 2019
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Path-specific counterfactual fairness
Silvia Chiappa · 2019
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Matroids, matchings, and fairness
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Fair transfer learning with missing protected attributes
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Optimization with non-differentiable constraints with applications to fairness, recall, churn, and other goals
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Bayesian fairness
Christos Dimitrakakis, Yang Liu, David C Parkes, and Goran Radanovic · 2019
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Fair algorithms for learning in allocation problems
Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, and Zachary Schutzman · 2019
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Learning fair representations via an adversarial framework
Rui Feng, Yang Yang, Yuehan Lyu, Chenhao Tan, Yizhou Sun, and Chunping Wang · 2019
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A general framework for fair regression
Jack Fitzsimons, AbdulRahman Al Ali, Michael Osborne, and Stephen Roberts · 2019
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A comparative study of fairness-enhancing interventions in machine learning
Sorelle A Friedler, Carlos Scheidegger, Suresh Venkatasubramanian, Sonam Choudhary, Evan P Hamilton, and Derek Roth · 2019
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Measuring the biases that matter: The ethical and casual foundations for measures of fairness in algorithms
Bruce Glymour and Jonathan Herington · 2019
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Obtaining fairness using optimal transport theory
Paula Gordaliza, Eustasio Del Barrio, Gamboa Fabrice, and Jean-Michel Loubes · 2019
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The Price of Fairness - A Framework to Explore Trade-Offs in Algorithmic Fairness
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The disparate effects of strategic manipulation
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50 Years of Test (Un)Fairness: Lessons for Machine Learning
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Wasserstein fair classification
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An algorithm for removing sensitive information: application to race-independent recidivism prediction
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Censored and fair universal representations using generative adversarial models
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Fair decisions despite imperfect predictions
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Multiaccuracy: Black-box post-processing for fairness in classification
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ifair: Learning individually fair data representations for algorithmic decision making
Preethi Lahoti, Krishna P Gummadi, and Gerhard Weikum · 2019
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The implicit fairness criterion of unconstrained learning
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A survey on bias and fairness in machine learning
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The social cost of strategic classification
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Active fairness in algorithmic decision making
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Fair division without disparate impact
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Lessons for artificial intelligence from the study of natural stupidity
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Interventional fairness: Causal database repair for algorithmic fairness
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Integrating ethics within machine learning courses
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Fairness gan: Generating datasets with fairness properties using a generative adversarial network
Prasanna Sattigeri, Samuel C Hoffman, Vijil Chenthamarakshan, and Kush R Varshney · 2019
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Fair coresets and streaming algorithms for fair k-means
Melanie Schmidt, Chris Schwiegelshohn, and Christian Sohler · 2019
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Policy Learning for Fairness in Ranking
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Mathematical notions vs. human perception of fairness: A descriptive approach to fairness for machine learning
Megha Srivastava, Hoda Heidari, and Andreas Krause · 2019
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A framework for understanding unintended consequences of machine learning
Harini Suresh and John V Guttag · 2019
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Why machine learning may lead to unfairness: Evidence from risk assessment for juvenile justice in catalonia
Songül Tolan, Marius Miron, Emilia Gómez, and Carlos Castillo · 2019
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Fairness without harm: Decoupled classifiers with preference guarantees
Berk Ustun, Yang Liu, and David Parkes · 2019
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Hiring algorithms: An ethnography of fairness in practice
Elmira van den Broek, Anastasia Sergeeva, and Marleen Huysman · 2019
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Repairing without retraining: Avoiding disparate impact with counterfactual distributions
Hao Wang, Berk Ustun, and Flavio Calmon · 2019
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Democratizing algorithmic fairness
Pak-Hang Wong · 2019
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Achieving causal fairness through generative adversarial networks
Depeng Xu, Yongkai Wu, Shuhan Yuan, Lu Zhang, and Xintao Wu · 2019
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Matching code and law: Achieving algorithmic fairness with optimal transport
Meike Zehlike, Philipp Hacker, and Emil Wiedemann · 2019
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Mitigation of unintended biases against non-native english texts in sentiment analysis
Alina Zhiltsova, Simon Caton, and Catherine Mulwa · 2019
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Counterfactual fairness: removing direct effects through regularization
Pietro G Di Stefano, James M Hickey, and Vlasios Vasileiou · 2020
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The false promise of risk assessments: Epistemic reform and the limits of fairness
Ben Green · 2020
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Algorithmic realism: Expanding the boundaries of algorithmic thought
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